Mask Visualization

Class: MaskVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.mask.v1.MaskVisualizationBlockV1

The MaskVisualization block uses a detected polygon from an instance segmentation to draw a mask using sv.MaskAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/mask_visualization@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
copy_image	bool	Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
opacity	float	Transparency of the Mask overlay..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Mask Visualization in version v1.

• inputs: Halo Visualization, Polygon Zone Visualization, Keypoint Detection Model, Label Visualization, Color Visualization, Clip Comparison, Relative Static Crop, VLM as Classifier, CSV Formatter, Stability AI Image Generation, Segment Anything 2 Model, Florence-2 Model, Circle Visualization, Roboflow Dataset Upload, LMM For Classification, Depth Estimation, Absolute Static Crop, Perspective Correction, Twilio SMS Notification, Google Gemini, Slack Notification, Detections Stitch, Grid Visualization, Image Preprocessing, Image Blur, Classification Label Visualization, Email Notification, Local File Sink, Image Contours, Florence-2 Model, SIFT Comparison, JSON Parser, Dimension Collapse, Instance Segmentation Model, Reference Path Visualization, Google Vision OCR, SIFT, Llama 3.2 Vision, Multi-Label Classification Model, Line Counter, Keypoint Visualization, Detections Filter, SIFT Comparison, CogVLM, Ellipse Visualization, Roboflow Dataset Upload, Triangle Visualization, Line Counter Visualization, VLM as Detector, Dynamic Zone, Dot Visualization, Velocity, Clip Comparison, Detections Classes Replacement, Path Deviation, Template Matching, Path Deviation, VLM as Classifier, Mask Visualization, Dynamic Crop, Object Detection Model, Stitch Images, Webhook Sink, Pixelate Visualization, Image Threshold, Corner Visualization, Roboflow Custom Metadata, Distance Measurement, Pixel Color Count, OpenAI, Model Comparison Visualization, Camera Calibration, Detections Stabilizer, Detections Consensus, Blur Visualization, Stability AI Inpainting, Line Counter, Time in Zone, Camera Focus, Image Slicer, Identify Changes, Trace Visualization, Time in Zone, OpenAI, Bounding Box Visualization, Stability AI Outpainting, Buffer, Identify Outliers, Crop Visualization, Stitch OCR Detections, Anthropic Claude, PTZ Tracking (ONVIF).md), Size Measurement, Detections Transformation, OpenAI, Detection Offset, Single-Label Classification Model, Image Slicer, Model Monitoring Inference Aggregator, Instance Segmentation Model, Bounding Rectangle, VLM as Detector, LMM, OCR Model, Image Convert Grayscale, Background Color Visualization, Polygon Visualization
• outputs: Multi-Label Classification Model, Polygon Zone Visualization, Halo Visualization, Keypoint Detection Model, Label Visualization, Color Visualization, Clip Comparison, QR Code Detection, Relative Static Crop, VLM as Classifier, Stability AI Image Generation, Segment Anything 2 Model, Florence-2 Model, Circle Visualization, Roboflow Dataset Upload, LMM For Classification, Depth Estimation, Absolute Static Crop, Perspective Correction, Google Gemini, Detections Stitch, Image Preprocessing, Image Blur, Classification Label Visualization, Image Contours, Florence-2 Model, YOLO-World Model, Instance Segmentation Model, Reference Path Visualization, Google Vision OCR, Object Detection Model, SIFT, Multi-Label Classification Model, Llama 3.2 Vision, Keypoint Visualization, Single-Label Classification Model, CogVLM, SIFT Comparison, Gaze Detection, Ellipse Visualization, Roboflow Dataset Upload, Triangle Visualization, Line Counter Visualization, Perception Encoder Embedding Model, VLM as Detector, Dot Visualization, Clip Comparison, Template Matching, Barcode Detection, VLM as Classifier, Mask Visualization, Object Detection Model, Dynamic Crop, CLIP Embedding Model, Stitch Images, Moondream2, Pixelate Visualization, SmolVLM2, Image Threshold, Corner Visualization, Byte Tracker, Pixel Color Count, OpenAI, Model Comparison Visualization, Camera Calibration, Qwen2.5-VL, Detections Stabilizer, Blur Visualization, Stability AI Inpainting, Camera Focus, Image Slicer, OpenAI, Trace Visualization, Time in Zone, Dominant Color, Bounding Box Visualization, Stability AI Outpainting, Buffer, Crop Visualization, Anthropic Claude, OpenAI, Keypoint Detection Model, Single-Label Classification Model, Instance Segmentation Model, Image Slicer, LMM, OCR Model, VLM as Detector, Image Convert Grayscale, Background Color Visualization, Polygon Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Mask Visualization in version v1 has.

Bindings

• input

  • image (image): The image to visualize on..
  • copy_image (boolean): Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..
  • predictions (instance_segmentation_prediction): Predictions.
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • opacity (float_zero_to_one): Transparency of the Mask overlay..

• output

  • image (image): Image in workflows.

Example JSON definition of step Mask Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/mask_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.instance_segmentation_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "opacity": 0.5
}